RESUMO
The purpose of a non-inferiority trial is to assert the efficacy of an experimental treatment compared with a reference treatment by showing that the experimental treatment retains a substantial proportion of the efficacy of the reference treatment. Statistical methods have been developed to test multiple experimental treatments in three-arm non-inferiority trials. In this paper, we report the development of procedures that simultaneously test the non-inferiority and the superiority of experimental treatments after the assay sensitivity has been established. The advantage of the proposed test procedures is the additional ability to identify superior treatments while retaining an non-inferiority testing power comparable to that of existing testing procedures. Single-step and stepwise procedures are derived and then compared with each other to determine their relative testing power and testing error in a simulation study. Finally, the suggested procedures are illustrated with two clinical examples.
Assuntos
Estudos Clínicos como Assunto/métodos , Interpretação Estatística de Dados , Terapias em Estudo/normas , Algoritmos , Estudos Clínicos como Assunto/estatística & dados numéricos , Humanos , Terapias em Estudo/estatística & dados numéricosRESUMO
Non-inferiority (NI) trials are becoming more popular. The NI of a new treatment compared with a standard treatment is established when the new treatment maintains a substantial fraction of the treatment effect of the standard treatment. A valid NI trial is also required to show assay sensitivity, the demonstration of the standard treatment having the expected effect with a size comparable to those reported in previous placebo-controlled studies. A three-arm NI trial is a clinical study that includes a new treatment, a standard treatment and a placebo. Most of the statistical methods developed for three-arm NI trials are designed for the existence of only one new treatment. Recently, a single-step procedure was developed to deal with NI trials with multiple new treatments with the overall familywise error rate controlled at a specified level. In this article, we extend the single-step procedure to two new step-up procedures for NI trials with multiple new treatments. A comparative study of test power shows that both proposed step-up procedures provide a significant improvement of power when compared to the single-step procedure. One of the two proposed step-up procedures also allows the flexibility of allocating different error rates between the sensitivity hypothesis and the NI hypotheses so that the assignment of fewer patients to the placebo becomes possible when designing NI trials. We illustrate the new procedures using data from a clinical trial.
Assuntos
Estudos de Equivalência como Asunto , Humanos , Projetos de PesquisaRESUMO
In clinical studies, multiple comparisons of several treatments to a control with ordered categorical responses are often encountered. A popular statistical approach to analyzing the data is to use the logistic regression model with the proportional odds assumption. As discussed in several recent research papers, if the proportional odds assumption fails to hold, the undesirable consequence of an inflated familywise type I error rate may affect the validity of the clinical findings. To remedy the problem, a more flexible approach that uses the latent normal model with single-step and stepwise testing procedures has been recently proposed. In this paper, we introduce a step-up procedure that uses the correlation structure of test statistics under the latent normal model. A simulation study demonstrates the superiority of the proposed procedure to all existing testing procedures. Based on the proposed step-up procedure, we derive an algorithm that enables the determination of the total sample size and the sample size allocation scheme with a pre-determined level of test power before the onset of a clinical trial. A clinical example is presented to illustrate our proposed method.
Assuntos
Algoritmos , Ensaios Clínicos como Assunto/métodos , Interpretação Estatística de Dados , Modelos Estatísticos , Simulação por Computador , Fentanila/administração & dosagem , Humanos , Lidocaína/administração & dosagem , Dor/prevenção & controle , Tamanho da AmostraRESUMO
Non-inferiority (NI) trials are becoming increasingly popular. The main purpose of NI trials is to assert the efficacy of a new treatment compared with an active control by demonstrating that the new treatment maintains a substantial fraction of the treatment effect of the control. Most of the statistical testing procedures in this area have been developed for three-arm NI trials in which a new treatment is compared with an active control in the presence of a placebo. However, NI trials frequently involve comparisons of several new treatments with a control, such as in studies involving different doses of a new drug or different combinations of several new drugs. In seeking an adequate testing procedure for such cases, we use a new approach that modifies existing testing procedures to cover circumstances in which several new treatments are present. We also give methods and algorithms to produce the optimal sample size configuration. In addition, we also discuss the advantages of using different margins for the assay sensitivity test between the active control and the placebo and the NI test between the new treatments and the active control. We illustrate the new approach by using data from a clinical trial.
Assuntos
Algoritmos , Ensaios Clínicos como Assunto/métodos , Broncodilatadores/uso terapêutico , Humanos , Indanos/uso terapêutico , Doença Pulmonar Obstrutiva Crônica/tratamento farmacológico , Quinolonas/uso terapêutico , Tamanho da Amostra , Derivados da Escopolamina/uso terapêutico , Espirometria , Brometo de TiotrópioRESUMO
Step-up procedures have been shown to be powerful testing methods in clinical trials for comparisons of several treatments with a control. In this paper, a determination of the optimal sample size for a step-up procedure that allows a pre-specified power level to be attained is discussed. Various definitions of power, such as all-pairs power, any-pair power, per-pair power and average power, in one- and two-sided tests are considered. An extensive numerical study confirms that square root allocation of sample size among treatments provides a better approximation of the optimal sample size relative to equal allocation. Based on square root allocation, tables are constructed, and users can conveniently obtain the approximate required sample size for the selected configurations of parameters and power. For clinical studies with difficulties in recruiting patients or when additional subjects lead to a significant increase in cost, a more precise computation of the required sample size is recommended. In such circumstances, our proposed procedure may be adopted to obtain the optimal sample size. It is also found that, contrary to conventional belief, the optimal allocation may considerably reduce the total sample size requirement in certain cases. The determination of the required sample sizes using both allocation rules are illustrated with two examples in clinical studies.
Assuntos
Ensaios Clínicos como Assunto/estatística & dados numéricos , Projetos de Pesquisa , Humanos , Projetos de Pesquisa/estatística & dados numéricos , Tamanho da Amostra , Resultado do TratamentoRESUMO
In clinical studies, it is common to compare several treatments with a control. In such cases, the most popular statistical technique is the Dunnett procedure. However, the Dunnett procedure is designed to deal with particular families of inferences in which all hypotheses are either one sided or two sided. Recently, based on the minimization of average simultaneous confidence interval width, a single-step procedure was derived to handle more general inferential families that contained a mixture of one- and two-sided inferences. But that single-step procedure is unable to guarantee the condition of p-value consistency which means that when a hypothesis with a certain p-value is rejected, all other hypotheses with smaller p-values are also rejected. In this paper, we present a single-step procedure and two stepwise procedures which are p-value consistent. The two proposed stepwise procedures provide more powerful testing methods when compared with single-step procedures. The extent of their superiority is demonstrated with a simulation study of average power. Selected critical values are tabulated for the implementation of the three proposed procedures. Additional simulation studies provide evidence that the new stepwise procedures are robust to moderate changes in the underlying probability distributions, and the proposed step-up procedure is uniformly more powerful than the resampling-based Hochberg step-up approach in all considered distribution models. Finally, we provide a practical example with sample data extracted from a medical experiment.
Assuntos
Pesquisa Biomédica/estatística & dados numéricos , Interpretação Estatística de Dados , Modelos Estatísticos , Pesquisa Biomédica/métodos , Estados UnidosRESUMO
Comparing several treatments with a control is a common objective of clinical studies. However, existing procedures mainly deal with particular families of inferences in which all hypotheses are either one- or two-sided. In this article, we seek to develop a procedure which copes with a more general testing environment in which the family of inferences is composed of a mixture of one- and two-sided hypotheses. The proposed procedure provides a more flexible and powerful tool than the existing method. The superiority of this method is also substantiated by a simulation study of average power. Selected critical values are tabulated for the implementation of the proposed procedure. Finally, we provide an illustrative example with sample data extracted from a medical experiment.
Assuntos
Ensaios Clínicos como Assunto/métodos , Interpretação Estatística de Dados , Projetos de Pesquisa , Idoso , Anti-Inflamatórios/farmacologia , Celecoxib , Simulação por Computador , Humanos , Lactonas/farmacologia , Naproxeno/farmacologia , Pirazóis , Sódio/urina , Sulfonamidas/farmacologia , SulfonasRESUMO
In clinical studies, multiple superiority/equivalence testing procedures can be applied to classify a new treatment as superior, equivalent (same therapeutic effect), or inferior to each set of standard treatments. Previous stepwise approaches (Dunnett and Tamhane, 1997, Statistics in Medicine16, 2489-2506; Kwong, 2001, Journal of Statistical Planning and Inference 97, 359-366) are only appropriate for balanced designs. Unfortunately, the construction of similar tests for unbalanced designs is far more complex, with two major difficulties: (i) the ordering of test statistics for superiority may not be the same as the ordering of test statistics for equivalence; and (ii) the correlation structure of the test statistics is not equi-correlated but product-correlated. In this article, we seek to develop a two-stage testing procedure for unbalanced designs, which are very popular in clinical experiments. This procedure is a combination of step-up and single-step testing procedures, while the familywise error rate is proved to be controlled at a designated level. Furthermore, a simulation study is conducted to compare the average powers of the proposed procedure to those of the single-step procedure. In addition, a clinical example is provided to illustrate the application of the new procedure.